{"id":"W6980887059","doi":"","title":"Data-driven Fault Detection of Electric Motors Using Novel Convolutional Neural Network Designs and Integration of Adaptive Signal Processing","year":2025,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Botanical Research and Chemistry","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Fault detection and isolation; Fault (geology); Artificial neural network; Feature extraction; Pattern recognition (psychology); Convolutional neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007749598,0.0001583007,0.0002337765,0.00002482538,0.0001456115,0.0000240093,0.0003074462,0.0001950047,0.001089018],"category_scores_gemma":[0.00002596249,0.00009167998,0.00005598114,0.00067029,0.00005446581,0.0001993227,0.00009484137,0.0002594565,1.931117e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000726533,"about_ca_system_score_gemma":0.00008457986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003313311,"about_ca_topic_score_gemma":0.002177919,"domain_scores_codex":[0.9989749,0.00005984642,0.0001964713,0.0003313882,0.0002365886,0.0002008256],"domain_scores_gemma":[0.9992589,0.0001284226,0.0002597852,0.00004936893,0.0002355457,0.00006799299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004750834,0.00004381589,0.00009625303,0.00006248042,0.00003164707,0.000001163469,0.00001236295,0.0002167209,0.6337056,0.00003196114,0.00000617559,0.3653167],"study_design_scores_gemma":[0.001868594,0.002512778,0.03263327,0.002288043,0.0008551434,0.00001342295,0.008716113,0.7659587,0.177061,0.0003906332,0.006370222,0.00133205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864532,0.0005330652,0.004234853,0.00001280658,0.00006108055,0.0003574975,0.0003128413,0.00002756796,0.008007128],"genre_scores_gemma":[0.9912903,0.00003665029,0.000496994,0.000004288467,0.00007555602,5.3166e-7,0.0004054423,0.000001499693,0.007688693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7657419,"threshold_uncertainty_score":0.9998241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05788311020355424,"score_gpt":0.2477870184924227,"score_spread":0.1899039082888685,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}